Nvs Ho Eval

nvs-ho-eval

NVS-HO: A Benchmark for Novel View Synthesis of Handheld Objects — Musawar Ali et al. (arXiv:2602.05822, 2026)

What this evaluates

Evaluates novel view synthesis (NVS) methods on real-world handheld objects using only RGB inputs. It probes a model's ability to reconstruct 3D-consistent renderings from unconstrained, handheld camera trajectories that exhibit motion blur, occlusions, and pose estimation inaccuracies.

Datasets

• NVS-HO — total ?; splits: train (-1), test (-1)

Metrics

• PSNR (primary) — range: other (dB)
  • Peak Signal-to-Noise Ratio computed on masked image pairs (object masks applied to both rendered and ground-truth views). Higher values indicate better pixel-level reconstruction fidelity.
• SSIM — range: [0, 1]
  • Structural Similarity Index computed on masked image pairs. Measures perceptual similarity between rendered and ground-truth images, with 1 indicating identical structure.
• LPIPS — range: [0, 1]
  • Learned Perceptual Image Patch Similarity computed on masked image pairs. Lower values indicate better perceptual similarity according to deep feature distances.

Input / output format

Input: RGB image sequences of handheld objects, along with camera poses estimated via COLMAP or VGGT.

Output: Rendered novel view images for each test viewpoint.

Scoring recipe

def compute_nvs_metrics(rendered_images, gt_images, object_masks):
    # Align rendered and ground-truth views in a common 3D reference frame
    # Apply object masks to filter background
    rendered_masked = [r * m for r, m in zip(rendered_images, object_masks)]
    gt_masked = [g * m for g, m in zip(gt_images, object_masks)]
    # Compute metrics per image pair
    psnr_scores = [compute_psnr(r, g) for r, g in zip(rendered_masked, gt_masked)]
    ssim_scores = [compute_ssim(r, g) for r, g in zip(rendered_masked, gt_masked)]
    lpips_scores = [compute_lpips(r, g) for r, g in zip(rendered_masked, gt_masked)]
    return mean(psnr_scores), mean(ssim_scores), mean(lpips_scores)

Common pitfalls

• Motion blur and occlusions in handheld sequences significantly degrade pose estimation and rendering quality, leading to low absolute metric values.
• Absolute metric values (e.g., median PSNR ~16) are much lower than in controlled setups, so relative improvements between methods are more meaningful than absolute scores.
• Pose refinement during training yields only marginal gains, indicating that accurate pose initialization is the primary bottleneck rather than optimization strategy.

Evidence (verbatim from paper)

The method-agnostic evaluation protocol aligns rendered and ground-truth views in a common 3D reference frame, enabling fair comparison of masked image pairs via PSNR, SSIM, and LPIPS.

Citation

@misc{ali2026nvs-ho,
  title={NVS-HO: A Benchmark for Novel View Synthesis of Handheld Objects},
  author={Musawar Ali et al.},
  year={2026},
  note={arXiv:2602.05822}
}

• arXiv: 2602.05822